Re-Imagining the Patient Panel: Introducing Lived Experiences of Psychosis into the Pre-clerkship Psychiatry Curriculum of a Canadian Medical School
Bibliographic record
Abstract
The involvement of people with lived experience (patients) in medical education offers a unique opportunity for students and residents to access personal and collective knowledge about the lived experience of health, ill health, and medical care. Involvement also has the potential to elevate the role of people with lived experience and their knowledge within medicine by providing a model for meaningful collaboration and partnership. However, involvement has been critiqued by critical disability scholars for its potential to harm without leading to meaningful change in professional knowledge or practice. In this article, we (two educators with lived experience and an academic psychiatrist) describe the development and delivery of an annual lived-experience presentation about psychosis for the second-year class of a large, urban medical school in Canada. We describe our reflexive process attempting to enact meaningful involvement and disrupt the uneven power relations that shape and constrain this work, in a setting where the risks of exploitation, tokenism, and co-optation are significant. Our goal has been to re-imagine the "patient panel," which puts significant limits on the position of patients as knowers. By re-defining roles and shifting power from faculty to lived experience educators, we have aimed to present important non-medical ideas about psychosis and how to effectively support people who experience it, while disrupting interpersonal and structural bias.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.035 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".